Overview

Dataset statistics

Number of variables56
Number of observations197905
Missing cells395810
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory399.0 MiB
Average record size in memory2.1 KiB

Variable types

Numeric15
Categorical36
Boolean3
Unsupported2

Alerts

tx_type has constant value "TRANSFER" Constant
type_orig has constant value "I" Constant
acct_stat_orig has constant value "A" Constant
acct_rptng_crncy_orig has constant value "USD" Constant
branch_id_orig has constant value "1" Constant
open_dt_orig has constant value "0" Constant
close_dt_orig has constant value "1000000" Constant
bank_id_orig has constant value "bank" Constant
country_orig has constant value "US" Constant
type_bene has constant value "I" Constant
acct_stat_bene has constant value "A" Constant
acct_rptng_crncy_bene has constant value "USD" Constant
branch_id_bene has constant value "1" Constant
open_dt_bene has constant value "0" Constant
close_dt_bene has constant value "1000000" Constant
bank_id_bene has constant value "bank" Constant
country_bene has constant value "US" Constant
tran_timestamp has a high cardinality: 720 distinct values High cardinality
dsply_nm_orig has a high cardinality: 2090 distinct values High cardinality
first_name_orig has a high cardinality: 479 distinct values High cardinality
last_name_orig has a high cardinality: 715 distinct values High cardinality
street_addr_orig has a high cardinality: 2090 distinct values High cardinality
city_orig has a high cardinality: 1924 distinct values High cardinality
state_orig has a high cardinality: 51 distinct values High cardinality
birth_date_orig has a high cardinality: 2039 distinct values High cardinality
ssn_orig has a high cardinality: 2090 distinct values High cardinality
dsply_nm_bene has a high cardinality: 4077 distinct values High cardinality
first_name_bene has a high cardinality: 582 distinct values High cardinality
last_name_bene has a high cardinality: 874 distinct values High cardinality
street_addr_bene has a high cardinality: 4077 distinct values High cardinality
city_bene has a high cardinality: 3546 distinct values High cardinality
state_bene has a high cardinality: 51 distinct values High cardinality
birth_date_bene has a high cardinality: 3883 distinct values High cardinality
ssn_bene has a high cardinality: 4077 distinct values High cardinality
orig_acct is highly correlated with acct_id_origHigh correlation
bene_acct is highly correlated with acct_id_beneHigh correlation
is_sar is highly correlated with alert_idHigh correlation
alert_id is highly correlated with is_sarHigh correlation
acct_id_orig is highly correlated with orig_acctHigh correlation
acct_id_bene is highly correlated with bene_acctHigh correlation
orig_acct is highly correlated with acct_id_origHigh correlation
bene_acct is highly correlated with acct_id_beneHigh correlation
is_sar is highly correlated with alert_idHigh correlation
alert_id is highly correlated with is_sarHigh correlation
acct_id_orig is highly correlated with orig_acctHigh correlation
acct_id_bene is highly correlated with bene_acctHigh correlation
orig_acct is highly correlated with acct_id_origHigh correlation
bene_acct is highly correlated with acct_id_beneHigh correlation
is_sar is highly correlated with alert_idHigh correlation
alert_id is highly correlated with is_sarHigh correlation
acct_id_orig is highly correlated with orig_acctHigh correlation
acct_id_bene is highly correlated with bene_acctHigh correlation
type_bene is highly correlated with bank_id_bene and 22 other fieldsHigh correlation
bank_id_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
country_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
close_dt_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
state_orig is highly correlated with type_bene and 16 other fieldsHigh correlation
tx_type is highly correlated with type_bene and 22 other fieldsHigh correlation
open_dt_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
acct_rptng_crncy_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
prior_sar_count_bene is highly correlated with type_bene and 17 other fieldsHigh correlation
bank_id_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
open_dt_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
type_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
is_sar is highly correlated with type_bene and 16 other fieldsHigh correlation
state_bene is highly correlated with type_bene and 18 other fieldsHigh correlation
gender_orig is highly correlated with type_bene and 16 other fieldsHigh correlation
acct_stat_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
gender_bene is highly correlated with type_bene and 17 other fieldsHigh correlation
branch_id_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
prior_sar_count_orig is highly correlated with type_bene and 16 other fieldsHigh correlation
country_bene is highly correlated with type_bene and 22 other fieldsHigh correlation
acct_rptng_crncy_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
close_dt_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
branch_id_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
acct_stat_orig is highly correlated with type_bene and 22 other fieldsHigh correlation
orig_acct is highly correlated with is_sar and 2 other fieldsHigh correlation
bene_acct is highly correlated with acct_id_beneHigh correlation
is_sar is highly correlated with orig_acct and 2 other fieldsHigh correlation
alert_id is highly correlated with orig_acct and 2 other fieldsHigh correlation
acct_id_orig is highly correlated with orig_acct and 2 other fieldsHigh correlation
state_orig is highly correlated with state_beneHigh correlation
acct_id_bene is highly correlated with bene_acctHigh correlation
prior_sar_count_bene is highly correlated with state_beneHigh correlation
initial_deposit_bene is highly correlated with state_bene and 3 other fieldsHigh correlation
state_bene is highly correlated with state_orig and 6 other fieldsHigh correlation
zip_bene is highly correlated with initial_deposit_bene and 3 other fieldsHigh correlation
gender_bene is highly correlated with state_beneHigh correlation
lon_bene is highly correlated with initial_deposit_bene and 3 other fieldsHigh correlation
lat_bene is highly correlated with initial_deposit_bene and 3 other fieldsHigh correlation
tx_behavior_id_orig has 197905 (100.0%) missing values Missing
tx_behavior_id_bene has 197905 (100.0%) missing values Missing
alert_id is highly skewed (γ1 = 21.92226068) Skewed
tran_id is uniformly distributed Uniform
tran_id has unique values Unique
tx_behavior_id_orig is an unsupported type, check if it needs cleaning or further analysis Unsupported
tx_behavior_id_bene is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-05 08:35:40.372160
Analysis finished2022-09-05 08:40:51.754415
Duration5 minutes and 11.38 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

tran_id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct197905
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98953
Minimum1
Maximum197905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:40:52.610154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9896.2
Q149477
median98953
Q3148429
95-th percentile188009.8
Maximum197905
Range197904
Interquartile range (IQR)98952

Descriptive statistics

Standard deviation57130.39685
Coefficient of variation (CV)0.5773488105
Kurtosis-1.2
Mean98953
Median Absolute Deviation (MAD)49476
Skewness0
Sum1.958329346 × 1010
Variance3263882244
MonotonicityStrictly increasing
2022-09-05T10:40:53.654079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
1829241
 
< 0.1%
334771
 
< 0.1%
396221
 
< 0.1%
375751
 
< 0.1%
601041
 
< 0.1%
580571
 
< 0.1%
642021
 
< 0.1%
621551
 
< 0.1%
519161
 
< 0.1%
Other values (197895)197895
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1979051
< 0.1%
1979041
< 0.1%
1979031
< 0.1%
1979021
< 0.1%
1979011
< 0.1%
1979001
< 0.1%
1978991
< 0.1%
1978981
< 0.1%
1978971
< 0.1%
1978961
< 0.1%

orig_acct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1909.848776
Minimum0
Maximum12007
Zeros103
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:40:54.849347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95
Q1461
median2098
Q32738
95-th percentile4711
Maximum12007
Range12007
Interquartile range (IQR)2277

Descriptive statistics

Standard deviation1618.01312
Coefficient of variation (CV)0.8471943644
Kurtosis-0.5802615119
Mean1909.848776
Median Absolute Deviation (MAD)1545
Skewness0.5934536229
Sum377968622
Variance2617966.456
MonotonicityNot monotonic
2022-09-05T10:40:55.987592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2486310
 
0.2%
2584278
 
0.1%
2696207
 
0.1%
2671207
 
0.1%
533207
 
0.1%
2751207
 
0.1%
654206
 
0.1%
392206
 
0.1%
485206
 
0.1%
570206
 
0.1%
Other values (2080)195665
98.9%
ValueCountFrequency (%)
0103
0.1%
1104
0.1%
2103
0.1%
3103
0.1%
4103
0.1%
5103
0.1%
6103
0.1%
7103
0.1%
8111
0.1%
9102
0.1%
ValueCountFrequency (%)
120071
< 0.1%
119901
< 0.1%
119861
< 0.1%
119741
< 0.1%
119681
< 0.1%
118711
< 0.1%
118581
< 0.1%
118401
< 0.1%
118371
< 0.1%
118271
< 0.1%

bene_acct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.6764811
Minimum0
Maximum11991
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:40:57.016049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q124
median53
Q3191
95-th percentile4149
Maximum11991
Range11991
Interquartile range (IQR)167

Descriptive statistics

Standard deviation1695.983714
Coefficient of variation (CV)2.977099757
Kurtosis19.95378574
Mean569.6764811
Median Absolute Deviation (MAD)38
Skewness4.364975752
Sum112741824
Variance2876360.758
MonotonicityNot monotonic
2022-09-05T10:40:58.078799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253563
 
1.8%
203263
 
1.6%
133210
 
1.6%
143103
 
1.6%
273017
 
1.5%
172953
 
1.5%
242917
 
1.5%
232917
 
1.5%
182800
 
1.4%
122682
 
1.4%
Other values (4067)167480
84.6%
ValueCountFrequency (%)
021
 
< 0.1%
1293
 
0.1%
2420
 
0.2%
31342
0.7%
4859
0.4%
51295
0.7%
61378
0.7%
71746
0.9%
81659
0.8%
91563
0.8%
ValueCountFrequency (%)
119911
 
< 0.1%
119901
 
< 0.1%
119741
 
< 0.1%
118763
 
< 0.1%
118711
 
< 0.1%
118581
 
< 0.1%
118401
 
< 0.1%
1182270
< 0.1%
117461
 
< 0.1%
117021
 
< 0.1%

tx_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
TRANSFER
197905 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1583240
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRANSFER
2nd rowTRANSFER
3rd rowTRANSFER
4th rowTRANSFER
5th rowTRANSFER

Common Values

ValueCountFrequency (%)
TRANSFER197905
100.0%

Length

2022-09-05T10:41:00.567611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:01.023730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
transfer197905
100.0%

Most occurring characters

ValueCountFrequency (%)
R395810
25.0%
T197905
12.5%
A197905
12.5%
N197905
12.5%
S197905
12.5%
F197905
12.5%
E197905
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1583240
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R395810
25.0%
T197905
12.5%
A197905
12.5%
N197905
12.5%
S197905
12.5%
F197905
12.5%
E197905
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1583240
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R395810
25.0%
T197905
12.5%
A197905
12.5%
N197905
12.5%
S197905
12.5%
F197905
12.5%
E197905
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1583240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R395810
25.0%
T197905
12.5%
A197905
12.5%
N197905
12.5%
S197905
12.5%
F197905
12.5%
E197905
12.5%

base_amt
Real number (ℝ≥0)

Distinct80654
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.6319096
Minimum0.09
Maximum999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:01.444348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile140.992
Q1319.58
median546.78
Q3772.29
95-th percentile954.37
Maximum999.99
Range999.9
Interquartile range (IQR)452.71

Descriptive statistics

Standard deviation261.6711649
Coefficient of variation (CV)0.4786972006
Kurtosis-1.197072321
Mean546.6319096
Median Absolute Deviation (MAD)226.38
Skewness0.0007397461412
Sum108181188.1
Variance68471.79854
MonotonicityNot monotonic
2022-09-05T10:41:02.006391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
565.5111
 
< 0.1%
247.4710
 
< 0.1%
124.6910
 
< 0.1%
553.7110
 
< 0.1%
988.7710
 
< 0.1%
166.9610
 
< 0.1%
110.2610
 
< 0.1%
855.8310
 
< 0.1%
122.8410
 
< 0.1%
122.799
 
< 0.1%
Other values (80644)197805
99.9%
ValueCountFrequency (%)
0.091
< 0.1%
0.161
< 0.1%
0.252
< 0.1%
0.431
< 0.1%
0.521
< 0.1%
0.611
< 0.1%
0.911
< 0.1%
0.941
< 0.1%
1.371
< 0.1%
1.51
< 0.1%
ValueCountFrequency (%)
999.991
 
< 0.1%
999.983
< 0.1%
999.975
< 0.1%
999.961
 
< 0.1%
999.954
< 0.1%
999.941
 
< 0.1%
999.933
< 0.1%
999.911
 
< 0.1%
999.91
 
< 0.1%
999.894
< 0.1%

tran_timestamp
Categorical

HIGH CARDINALITY

Distinct720
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
2017-05-24T00:00:00Z
 
363
2017-04-26T00:00:00Z
 
363
2017-02-15T00:00:00Z
 
361
2017-06-14T00:00:00Z
 
359
2017-02-08T00:00:00Z
 
359
Other values (715)
196100 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters3958100
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-01T00:00:00Z
2nd row2017-01-01T00:00:00Z
3rd row2017-01-01T00:00:00Z
4th row2017-01-01T00:00:00Z
5th row2017-01-01T00:00:00Z

Common Values

ValueCountFrequency (%)
2017-05-24T00:00:00Z363
 
0.2%
2017-04-26T00:00:00Z363
 
0.2%
2017-02-15T00:00:00Z361
 
0.2%
2017-06-14T00:00:00Z359
 
0.2%
2017-02-08T00:00:00Z359
 
0.2%
2017-02-01T00:00:00Z359
 
0.2%
2017-05-03T00:00:00Z359
 
0.2%
2017-06-21T00:00:00Z359
 
0.2%
2017-04-19T00:00:00Z358
 
0.2%
2017-05-17T00:00:00Z358
 
0.2%
Other values (710)194307
98.2%

Length

2022-09-05T10:41:02.425414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-05-24t00:00:00z363
 
0.2%
2017-04-26t00:00:00z363
 
0.2%
2017-02-15t00:00:00z361
 
0.2%
2017-02-08t00:00:00z359
 
0.2%
2017-02-01t00:00:00z359
 
0.2%
2017-05-03t00:00:00z359
 
0.2%
2017-06-21t00:00:00z359
 
0.2%
2017-06-14t00:00:00z359
 
0.2%
2017-06-28t00:00:00z358
 
0.2%
2017-01-11t00:00:00z358
 
0.2%
Other values (710)194307
98.2%

Most occurring characters

ValueCountFrequency (%)
01633970
41.3%
-395810
 
10.0%
:395810
 
10.0%
1364403
 
9.2%
2310090
 
7.8%
T197905
 
5.0%
Z197905
 
5.0%
7155858
 
3.9%
8114761
 
2.9%
346883
 
1.2%
Other values (4)144705
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2770670
70.0%
Dash Punctuation395810
 
10.0%
Other Punctuation395810
 
10.0%
Uppercase Letter395810
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01633970
59.0%
1364403
 
13.2%
2310090
 
11.2%
7155858
 
5.6%
8114761
 
4.1%
346883
 
1.7%
537164
 
1.3%
436940
 
1.3%
636153
 
1.3%
934448
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
T197905
50.0%
Z197905
50.0%
Dash Punctuation
ValueCountFrequency (%)
-395810
100.0%
Other Punctuation
ValueCountFrequency (%)
:395810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3562290
90.0%
Latin395810
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01633970
45.9%
-395810
 
11.1%
:395810
 
11.1%
1364403
 
10.2%
2310090
 
8.7%
7155858
 
4.4%
8114761
 
3.2%
346883
 
1.3%
537164
 
1.0%
436940
 
1.0%
Other values (2)70601
 
2.0%
Latin
ValueCountFrequency (%)
T197905
50.0%
Z197905
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3958100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01633970
41.3%
-395810
 
10.0%
:395810
 
10.0%
1364403
 
9.2%
2310090
 
7.8%
T197905
 
5.0%
Z197905
 
5.0%
7155858
 
3.9%
8114761
 
2.9%
346883
 
1.2%
Other values (4)144705
 
3.7%

is_sar
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
197234 
True
 
671
ValueCountFrequency (%)
False197234
99.7%
True671
 
0.3%
2022-09-05T10:41:02.761650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

alert_id
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8235466512
Minimum-1
Maximum99
Zeros7
Zeros (%)< 0.1%
Negative197234
Negative (%)99.7%
Memory size3.0 MiB
2022-09-05T10:41:03.117453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.472224916
Coefficient of variation (CV)-4.216184853
Kurtosis507.7623303
Mean-0.8235466512
Median Absolute Deviation (MAD)0
Skewness21.92226068
Sum-162984
Variance12.05634587
MonotonicityNot monotonic
2022-09-05T10:41:03.516336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1197234
99.7%
7410
 
< 0.1%
7910
 
< 0.1%
7810
 
< 0.1%
8310
 
< 0.1%
6210
 
< 0.1%
359
 
< 0.1%
739
 
< 0.1%
699
 
< 0.1%
199
 
< 0.1%
Other values (91)585
 
0.3%
ValueCountFrequency (%)
-1197234
99.7%
07
 
< 0.1%
15
 
< 0.1%
27
 
< 0.1%
38
 
< 0.1%
45
 
< 0.1%
56
 
< 0.1%
68
 
< 0.1%
78
 
< 0.1%
89
 
< 0.1%
ValueCountFrequency (%)
997
< 0.1%
986
< 0.1%
976
< 0.1%
968
< 0.1%
959
< 0.1%
947
< 0.1%
935
< 0.1%
926
< 0.1%
918
< 0.1%
907
< 0.1%

acct_id_orig
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1909.848776
Minimum0
Maximum12007
Zeros103
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:03.956201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95
Q1461
median2098
Q32738
95-th percentile4711
Maximum12007
Range12007
Interquartile range (IQR)2277

Descriptive statistics

Standard deviation1618.01312
Coefficient of variation (CV)0.8471943644
Kurtosis-0.5802615119
Mean1909.848776
Median Absolute Deviation (MAD)1545
Skewness0.5934536229
Sum377968622
Variance2617966.456
MonotonicityNot monotonic
2022-09-05T10:41:04.490625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2486310
 
0.2%
2584278
 
0.1%
2696207
 
0.1%
2671207
 
0.1%
533207
 
0.1%
2751207
 
0.1%
654206
 
0.1%
392206
 
0.1%
485206
 
0.1%
570206
 
0.1%
Other values (2080)195665
98.9%
ValueCountFrequency (%)
0103
0.1%
1104
0.1%
2103
0.1%
3103
0.1%
4103
0.1%
5103
0.1%
6103
0.1%
7103
0.1%
8111
0.1%
9102
0.1%
ValueCountFrequency (%)
120071
< 0.1%
119901
< 0.1%
119861
< 0.1%
119741
< 0.1%
119681
< 0.1%
118711
< 0.1%
118581
< 0.1%
118401
< 0.1%
118371
< 0.1%
118271
< 0.1%

dsply_nm_orig
Categorical

HIGH CARDINALITY

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
C_2486
 
310
C_2584
 
278
C_2696
 
207
C_533
 
207
C_2671
 
207
Other values (2085)
196696 

Length

Max length7
Median length6
Mean length5.473257371
Min length3

Characters and Unicode

Total characters1083185
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)0.2%

Sample

1st rowC_4376
2nd rowC_4300
3rd rowC_4433
4th rowC_2552
5th rowC_2552

Common Values

ValueCountFrequency (%)
C_2486310
 
0.2%
C_2584278
 
0.1%
C_2696207
 
0.1%
C_533207
 
0.1%
C_2671207
 
0.1%
C_2751207
 
0.1%
C_392206
 
0.1%
C_639206
 
0.1%
C_557206
 
0.1%
C_574206
 
0.1%
Other values (2080)195665
98.9%

Length

2022-09-05T10:41:05.022469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c_2486310
 
0.2%
c_2584278
 
0.1%
c_2696207
 
0.1%
c_2751207
 
0.1%
c_533207
 
0.1%
c_2671207
 
0.1%
c_465206
 
0.1%
c_392206
 
0.1%
c_654206
 
0.1%
c_389206
 
0.1%
Other values (2080)195665
98.9%

Most occurring characters

ValueCountFrequency (%)
C197905
18.3%
_197905
18.3%
2116165
10.7%
490370
8.3%
672745
 
6.7%
571985
 
6.6%
367942
 
6.3%
763083
 
5.8%
162481
 
5.8%
948886
 
4.5%
Other values (2)93718
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number687375
63.5%
Uppercase Letter197905
 
18.3%
Connector Punctuation197905
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2116165
16.9%
490370
13.1%
672745
10.6%
571985
10.5%
367942
9.9%
763083
9.2%
162481
9.1%
948886
7.1%
047666
6.9%
846052
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C197905
100.0%
Connector Punctuation
ValueCountFrequency (%)
_197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common885280
81.7%
Latin197905
 
18.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_197905
22.4%
2116165
13.1%
490370
10.2%
672745
 
8.2%
571985
 
8.1%
367942
 
7.7%
763083
 
7.1%
162481
 
7.1%
948886
 
5.5%
047666
 
5.4%
Latin
ValueCountFrequency (%)
C197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1083185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C197905
18.3%
_197905
18.3%
2116165
10.7%
490370
8.3%
672745
 
6.7%
571985
 
6.6%
367942
 
6.3%
763083
 
5.8%
162481
 
5.8%
948886
 
4.5%
Other values (2)93718
8.7%

type_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
I
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I197905
100.0%

Length

2022-09-05T10:41:05.462405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:05.772917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
i197905
100.0%

Most occurring characters

ValueCountFrequency (%)
I197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter197905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin197905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I197905
100.0%

acct_stat_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
A
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A197905
100.0%

Length

2022-09-05T10:41:05.970190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:06.228534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a197905
100.0%

Most occurring characters

ValueCountFrequency (%)
A197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter197905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin197905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A197905
100.0%

acct_rptng_crncy_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 MiB
USD
197905 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters593715
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD197905
100.0%

Length

2022-09-05T10:41:06.693876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:07.203057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
usd197905
100.0%

Most occurring characters

ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter593715
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin593715
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII593715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

prior_sar_count_orig
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
174324 
True
23581 
ValueCountFrequency (%)
False174324
88.1%
True23581
 
11.9%
2022-09-05T10:41:07.551599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

branch_id_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
1
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1197905
100.0%

Length

2022-09-05T10:41:07.940230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:08.398126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1197905
100.0%

Most occurring characters

ValueCountFrequency (%)
1197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number197905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common197905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1197905
100.0%

open_dt_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
0
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0197905
100.0%

Length

2022-09-05T10:41:08.662861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:08.899281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0197905
100.0%

Most occurring characters

ValueCountFrequency (%)
0197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number197905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common197905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0197905
100.0%

close_dt_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1000000
197905 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1385335
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000000
2nd row1000000
3rd row1000000
4th row1000000
5th row1000000

Common Values

ValueCountFrequency (%)
1000000197905
100.0%

Length

2022-09-05T10:41:09.126498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:09.371647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1000000197905
100.0%

Most occurring characters

ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1385335
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common1385335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1385335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

initial_deposit_orig
Real number (ℝ≥0)

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75597.28015
Minimum50009.28
Maximum99999.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:09.612524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50009.28
5-th percentile53209.65
Q163514.54
median76142.4
Q387290.86
95-th percentile97289.61
Maximum99999.31
Range49990.03
Interquartile range (IQR)23776.32

Descriptive statistics

Standard deviation13970.57272
Coefficient of variation (CV)0.1848025841
Kurtosis-1.157314352
Mean75597.28015
Median Absolute Deviation (MAD)11870.37
Skewness-0.04921636702
Sum1.496107973 × 1010
Variance195176902.1
MonotonicityNot monotonic
2022-09-05T10:41:09.912246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87199.11310
 
0.2%
62288.74278
 
0.1%
90983.71207
 
0.1%
92422.42207
 
0.1%
83668.85207
 
0.1%
77381.5207
 
0.1%
68754.28206
 
0.1%
92961.44206
 
0.1%
97514.84206
 
0.1%
75858.51206
 
0.1%
Other values (2080)195665
98.9%
ValueCountFrequency (%)
50009.281
 
< 0.1%
500501
 
< 0.1%
50058.695
< 0.1%
50060.391
 
< 0.1%
50110.37103
0.1%
50255.4994
< 0.1%
50261.31108
0.1%
50291.361
 
< 0.1%
50295.9192
< 0.1%
50316.89130
0.1%
ValueCountFrequency (%)
99999.31103
0.1%
99951.81103
0.1%
99942.06103
0.1%
99932.76198
0.1%
99928.3197
0.1%
99915.41
 
< 0.1%
99870.09103
0.1%
99857.44103
0.1%
99703.4103
0.1%
99681.71
 
< 0.1%

tx_behavior_id_orig
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing197905
Missing (%)100.0%
Memory size3.0 MiB

bank_id_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
bank
197905 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters791620
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbank
2nd rowbank
3rd rowbank
4th rowbank
5th rowbank

Common Values

ValueCountFrequency (%)
bank197905
100.0%

Length

2022-09-05T10:41:10.220577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:10.466039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bank197905
100.0%

Most occurring characters

ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter791620
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin791620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII791620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

first_name_orig
Categorical

HIGH CARDINALITY

Distinct479
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Michael
 
5255
David
 
3252
Jennifer
 
3142
Robert
 
2805
John
 
2626
Other values (474)
180825 

Length

Max length11
Median length10
Mean length6.053424623
Min length2

Characters and Unicode

Total characters1198003
Distinct characters50
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)< 0.1%

Sample

1st rowKristin
2nd rowAnthony
3rd rowDiana
4th rowDebbie
5th rowDebbie

Common Values

ValueCountFrequency (%)
Michael5255
 
2.7%
David3252
 
1.6%
Jennifer3142
 
1.6%
Robert2805
 
1.4%
John2626
 
1.3%
Nicole2124
 
1.1%
Kevin2115
 
1.1%
James2093
 
1.1%
Daniel2075
 
1.0%
William1957
 
1.0%
Other values (469)170461
86.1%

Length

2022-09-05T10:41:10.694522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael5255
 
2.7%
david3252
 
1.6%
jennifer3142
 
1.6%
robert2805
 
1.4%
john2626
 
1.3%
nicole2124
 
1.1%
kevin2115
 
1.1%
james2093
 
1.1%
daniel2075
 
1.0%
william1957
 
1.0%
Other values (469)170461
86.1%

Most occurring characters

ValueCountFrequency (%)
a141751
 
11.8%
e125061
 
10.4%
i98176
 
8.2%
n93550
 
7.8%
r82797
 
6.9%
l71479
 
6.0%
h58627
 
4.9%
t48648
 
4.1%
o47601
 
4.0%
y41596
 
3.5%
Other values (40)388717
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1000098
83.5%
Uppercase Letter197905
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a141751
14.2%
e125061
12.5%
i98176
9.8%
n93550
9.4%
r82797
8.3%
l71479
 
7.1%
h58627
 
5.9%
t48648
 
4.9%
o47601
 
4.8%
y41596
 
4.2%
Other values (16)190812
19.1%
Uppercase Letter
ValueCountFrequency (%)
J27225
13.8%
M19484
9.8%
A18542
9.4%
S15633
 
7.9%
C15348
 
7.8%
K15055
 
7.6%
D14423
 
7.3%
R11201
 
5.7%
T11150
 
5.6%
B8107
 
4.1%
Other values (14)41737
21.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1198003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a141751
 
11.8%
e125061
 
10.4%
i98176
 
8.2%
n93550
 
7.8%
r82797
 
6.9%
l71479
 
6.0%
h58627
 
4.9%
t48648
 
4.1%
o47601
 
4.0%
y41596
 
3.5%
Other values (40)388717
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1198003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a141751
 
11.8%
e125061
 
10.4%
i98176
 
8.2%
n93550
 
7.8%
r82797
 
6.9%
l71479
 
6.0%
h58627
 
4.9%
t48648
 
4.1%
o47601
 
4.0%
y41596
 
3.5%
Other values (40)388717
32.4%

last_name_orig
Categorical

HIGH CARDINALITY

Distinct715
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Smith
 
4420
Jones
 
3321
Johnson
 
3124
Brown
 
2080
Williams
 
1959
Other values (710)
183001 

Length

Max length11
Median length10
Mean length6.065405119
Min length2

Characters and Unicode

Total characters1200374
Distinct characters51
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowGentry
2nd rowTaylor
3rd rowGray
4th rowJones
5th rowJones

Common Values

ValueCountFrequency (%)
Smith4420
 
2.2%
Jones3321
 
1.7%
Johnson3124
 
1.6%
Brown2080
 
1.1%
Williams1959
 
1.0%
Harris1818
 
0.9%
Garcia1784
 
0.9%
Miller1662
 
0.8%
Rodriguez1609
 
0.8%
Thomas1607
 
0.8%
Other values (705)174521
88.2%

Length

2022-09-05T10:41:10.955467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
smith4420
 
2.2%
jones3321
 
1.7%
johnson3124
 
1.6%
brown2080
 
1.1%
williams1959
 
1.0%
harris1818
 
0.9%
garcia1784
 
0.9%
miller1662
 
0.8%
rodriguez1609
 
0.8%
thomas1607
 
0.8%
Other values (705)174521
88.2%

Most occurring characters

ValueCountFrequency (%)
e119486
 
10.0%
r102199
 
8.5%
n102044
 
8.5%
o99049
 
8.3%
a94599
 
7.9%
s68886
 
5.7%
l66431
 
5.5%
i63874
 
5.3%
t45615
 
3.8%
h31883
 
2.7%
Other values (41)406308
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1002469
83.5%
Uppercase Letter197905
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e119486
11.9%
r102199
10.2%
n102044
10.2%
o99049
9.9%
a94599
9.4%
s68886
 
6.9%
l66431
 
6.6%
i63874
 
6.4%
t45615
 
4.6%
h31883
 
3.2%
Other values (16)208403
20.8%
Uppercase Letter
ValueCountFrequency (%)
M19263
 
9.7%
S19106
 
9.7%
H17601
 
8.9%
R14518
 
7.3%
W14110
 
7.1%
B13773
 
7.0%
C12583
 
6.4%
P11913
 
6.0%
G11543
 
5.8%
J10654
 
5.4%
Other values (15)52841
26.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1200374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e119486
 
10.0%
r102199
 
8.5%
n102044
 
8.5%
o99049
 
8.3%
a94599
 
7.9%
s68886
 
5.7%
l66431
 
5.5%
i63874
 
5.3%
t45615
 
3.8%
h31883
 
2.7%
Other values (41)406308
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e119486
 
10.0%
r102199
 
8.5%
n102044
 
8.5%
o99049
 
8.3%
a94599
 
7.9%
s68886
 
5.7%
l66431
 
5.5%
i63874
 
5.3%
t45615
 
3.8%
h31883
 
2.7%
Other values (41)406308
33.8%

street_addr_orig
Categorical

HIGH CARDINALITY

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
43952 Caroline View
 
310
01225 Navarro Center Apt. 116
 
278
6752 Joanna Parkway
 
207
776 Diana Unions Apt. 503
 
207
920 Jose Mountain Suite 313
 
207
Other values (2085)
196696 

Length

Max length36
Median length30
Mean length22.44974104
Min length12

Characters and Unicode

Total characters4442916
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)0.2%

Sample

1st row9548 Hooper Manors
2nd row337 Robinson Oval
3rd row82286 Cheryl Station
4th row35710 Gary Summit
5th row35710 Gary Summit

Common Values

ValueCountFrequency (%)
43952 Caroline View310
 
0.2%
01225 Navarro Center Apt. 116278
 
0.1%
6752 Joanna Parkway207
 
0.1%
776 Diana Unions Apt. 503207
 
0.1%
920 Jose Mountain Suite 313207
 
0.1%
33291 Christina Freeway207
 
0.1%
364 Misty Islands Suite 079206
 
0.1%
8409 Wright Prairie Apt. 862206
 
0.1%
58202 Williams Plains Suite 698206
 
0.1%
64140 Nathan Bridge Suite 735206
 
0.1%
Other values (2080)195665
98.9%

Length

2022-09-05T10:41:11.270988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apt50266
 
6.3%
suite49008
 
6.2%
michael3119
 
0.4%
trail2670
 
0.3%
james2628
 
0.3%
jennifer2476
 
0.3%
locks2440
 
0.3%
drive2311
 
0.3%
ports2253
 
0.3%
mission2244
 
0.3%
Other values (3178)672848
84.9%

Most occurring characters

ValueCountFrequency (%)
594358
 
13.4%
e273330
 
6.2%
a220244
 
5.0%
i194501
 
4.4%
t192496
 
4.3%
r185417
 
4.2%
n158680
 
3.6%
s155340
 
3.5%
o145682
 
3.3%
l130420
 
2.9%
Other values (52)2192448
49.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2211438
49.8%
Decimal Number1091770
24.6%
Space Separator594358
 
13.4%
Uppercase Letter495084
 
11.1%
Other Punctuation50266
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e273330
12.4%
a220244
10.0%
i194501
8.8%
t192496
8.7%
r185417
 
8.4%
n158680
 
7.2%
s155340
 
7.0%
o145682
 
6.6%
l130420
 
5.9%
u90297
 
4.1%
Other values (16)465031
21.0%
Uppercase Letter
ValueCountFrequency (%)
S86949
17.6%
A65797
13.3%
C40282
 
8.1%
M34640
 
7.0%
P25061
 
5.1%
R24991
 
5.0%
J21702
 
4.4%
B21064
 
4.3%
L20935
 
4.2%
F19638
 
4.0%
Other values (14)134025
27.1%
Decimal Number
ValueCountFrequency (%)
5115543
10.6%
4112911
10.3%
1111498
10.2%
3111270
10.2%
2108910
10.0%
6108627
9.9%
8107798
9.9%
9106409
9.7%
7105336
9.6%
0103468
9.5%
Space Separator
ValueCountFrequency (%)
594358
100.0%
Other Punctuation
ValueCountFrequency (%)
.50266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2706522
60.9%
Common1736394
39.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e273330
 
10.1%
a220244
 
8.1%
i194501
 
7.2%
t192496
 
7.1%
r185417
 
6.9%
n158680
 
5.9%
s155340
 
5.7%
o145682
 
5.4%
l130420
 
4.8%
u90297
 
3.3%
Other values (40)960115
35.5%
Common
ValueCountFrequency (%)
594358
34.2%
5115543
 
6.7%
4112911
 
6.5%
1111498
 
6.4%
3111270
 
6.4%
2108910
 
6.3%
6108627
 
6.3%
8107798
 
6.2%
9106409
 
6.1%
7105336
 
6.1%
Other values (2)153734
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4442916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
594358
 
13.4%
e273330
 
6.2%
a220244
 
5.0%
i194501
 
4.4%
t192496
 
4.3%
r185417
 
4.2%
n158680
 
3.6%
s155340
 
3.5%
o145682
 
3.3%
l130420
 
2.9%
Other values (52)2192448
49.3%

city_orig
Categorical

HIGH CARDINALITY

Distinct1924
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
North Heather
 
523
Port David
 
470
West Matthew
 
449
New Megan
 
380
Williamstown
 
373
Other values (1919)
195710 

Length

Max length24
Median length20
Mean length12.01830171
Min length6

Characters and Unicode

Total characters2378482
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)0.2%

Sample

1st rowSarahmouth
2nd rowWest Kevintown
3rd rowNathanport
4th rowDavidbury
5th rowDavidbury

Common Values

ValueCountFrequency (%)
North Heather523
 
0.3%
Port David470
 
0.2%
West Matthew449
 
0.2%
New Megan380
 
0.2%
Williamstown373
 
0.2%
Smithmouth370
 
0.2%
Port Michael361
 
0.2%
Douglasfurt359
 
0.2%
North Daniel320
 
0.2%
Wilsonview312
 
0.2%
Other values (1914)193988
98.0%

Length

2022-09-05T10:41:11.588830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
port14945
 
5.1%
north14247
 
4.9%
west13376
 
4.6%
lake12884
 
4.4%
east12758
 
4.4%
south12111
 
4.2%
new12078
 
4.2%
michael1368
 
0.5%
david1122
 
0.4%
matthew1021
 
0.4%
Other values (1641)194394
67.0%

Most occurring characters

ValueCountFrequency (%)
e223797
 
9.4%
t193632
 
8.1%
r190821
 
8.0%
a181415
 
7.6%
o167361
 
7.0%
h141276
 
5.9%
n127565
 
5.4%
i119285
 
5.0%
s109225
 
4.6%
92399
 
3.9%
Other values (40)831706
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1995779
83.9%
Uppercase Letter290304
 
12.2%
Space Separator92399
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e223797
11.2%
t193632
9.7%
r190821
9.6%
a181415
 
9.1%
o167361
 
8.4%
h141276
 
7.1%
n127565
 
6.4%
i119285
 
6.0%
s109225
 
5.5%
l84057
 
4.2%
Other values (16)457345
22.9%
Uppercase Letter
ValueCountFrequency (%)
N30953
10.7%
S30404
10.5%
J24666
 
8.5%
W21204
 
7.3%
M20500
 
7.1%
P20333
 
7.0%
L20322
 
7.0%
E17652
 
6.1%
C16894
 
5.8%
D13672
 
4.7%
Other values (13)73704
25.4%
Space Separator
ValueCountFrequency (%)
92399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2286083
96.1%
Common92399
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e223797
 
9.8%
t193632
 
8.5%
r190821
 
8.3%
a181415
 
7.9%
o167361
 
7.3%
h141276
 
6.2%
n127565
 
5.6%
i119285
 
5.2%
s109225
 
4.8%
l84057
 
3.7%
Other values (39)747649
32.7%
Common
ValueCountFrequency (%)
92399
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2378482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e223797
 
9.4%
t193632
 
8.1%
r190821
 
8.0%
a181415
 
7.6%
o167361
 
7.0%
h141276
 
5.9%
n127565
 
5.4%
i119285
 
5.0%
s109225
 
4.6%
92399
 
3.9%
Other values (40)831706
35.0%

state_orig
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
WV
 
5193
TN
 
5102
UT
 
4853
KS
 
4772
NY
 
4659
Other values (46)
173326 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters395810
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIA
2nd rowNM
3rd rowMS
4th rowRI
5th rowRI

Common Values

ValueCountFrequency (%)
WV5193
 
2.6%
TN5102
 
2.6%
UT4853
 
2.5%
KS4772
 
2.4%
NY4659
 
2.4%
CT4620
 
2.3%
HI4614
 
2.3%
MO4597
 
2.3%
SC4542
 
2.3%
NE4530
 
2.3%
Other values (41)150423
76.0%

Length

2022-09-05T10:41:11.897247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv5193
 
2.6%
tn5102
 
2.6%
ut4853
 
2.5%
ks4772
 
2.4%
ny4659
 
2.4%
ct4620
 
2.3%
hi4614
 
2.3%
mo4597
 
2.3%
sc4542
 
2.3%
ne4530
 
2.3%
Other values (41)150423
76.0%

Most occurring characters

ValueCountFrequency (%)
N44356
 
11.2%
A42484
 
10.7%
M34633
 
8.7%
I28958
 
7.3%
C24897
 
6.3%
T24695
 
6.2%
D23943
 
6.0%
O20485
 
5.2%
S17752
 
4.5%
V16171
 
4.1%
Other values (14)117436
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter395810
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N44356
 
11.2%
A42484
 
10.7%
M34633
 
8.7%
I28958
 
7.3%
C24897
 
6.3%
T24695
 
6.2%
D23943
 
6.0%
O20485
 
5.2%
S17752
 
4.5%
V16171
 
4.1%
Other values (14)117436
29.7%

Most occurring scripts

ValueCountFrequency (%)
Latin395810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N44356
 
11.2%
A42484
 
10.7%
M34633
 
8.7%
I28958
 
7.3%
C24897
 
6.3%
T24695
 
6.2%
D23943
 
6.0%
O20485
 
5.2%
S17752
 
4.5%
V16171
 
4.1%
Other values (14)117436
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII395810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N44356
 
11.2%
A42484
 
10.7%
M34633
 
8.7%
I28958
 
7.3%
C24897
 
6.3%
T24695
 
6.2%
D23943
 
6.0%
O20485
 
5.2%
S17752
 
4.5%
V16171
 
4.1%
Other values (14)117436
29.7%

country_orig
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
US
197905 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters395810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US197905
100.0%

Length

2022-09-05T10:41:12.302137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:12.621180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
us197905
100.0%

Most occurring characters

ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter395810
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin395810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII395810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

zip_orig
Real number (ℝ≥0)

Distinct2072
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50396.09035
Minimum506
Maximum99947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:12.864037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile6309
Q125849
median49408
Q375606
95-th percentile94947
Maximum99947
Range99441
Interquartile range (IQR)49757

Descriptive statistics

Standard deviation28565.76163
Coefficient of variation (CV)0.5668249548
Kurtosis-1.196568599
Mean50396.09035
Median Absolute Deviation (MAD)25057
Skewness0.03126071938
Sum9973638261
Variance816002737.7
MonotonicityNot monotonic
2022-09-05T10:41:13.203773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61527310
 
0.2%
26692306
 
0.2%
49050278
 
0.1%
37074278
 
0.1%
90488271
 
0.1%
31983264
 
0.1%
56532258
 
0.1%
10641208
 
0.1%
98438207
 
0.1%
71374207
 
0.1%
Other values (2062)195318
98.7%
ValueCountFrequency (%)
506152
0.1%
608104
0.1%
6121
 
< 0.1%
7511
 
< 0.1%
759171
0.1%
886103
0.1%
90198
< 0.1%
922103
0.1%
9311
 
< 0.1%
997103
0.1%
ValueCountFrequency (%)
99947104
0.1%
99873198
0.1%
99868207
0.1%
99849103
0.1%
99757104
0.1%
996691
 
< 0.1%
99654103
0.1%
99489104
0.1%
99461103
0.1%
99396159
0.1%

gender_orig
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.2 MiB
Female
100200 
Male
97705 

Length

Max length6
Median length6
Mean length5.012607059
Min length4

Characters and Unicode

Total characters992020
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female100200
50.6%
Male97705
49.4%

Length

2022-09-05T10:41:13.534168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:13.800079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female100200
50.6%
male97705
49.4%

Most occurring characters

ValueCountFrequency (%)
e298105
30.1%
a197905
19.9%
l197905
19.9%
F100200
 
10.1%
m100200
 
10.1%
M97705
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter794115
80.1%
Uppercase Letter197905
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e298105
37.5%
a197905
24.9%
l197905
24.9%
m100200
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F100200
50.6%
M97705
49.4%

Most occurring scripts

ValueCountFrequency (%)
Latin992020
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e298105
30.1%
a197905
19.9%
l197905
19.9%
F100200
 
10.1%
m100200
 
10.1%
M97705
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII992020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e298105
30.1%
a197905
19.9%
l197905
19.9%
F100200
 
10.1%
m100200
 
10.1%
M97705
 
9.8%

birth_date_orig
Categorical

HIGH CARDINALITY

Distinct2039
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size14.2 MiB
2000-12-20
 
310
1980-03-09
 
300
1987-06-11
 
278
1993-03-12
 
268
1966-02-08
 
265
Other values (2034)
196484 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1979050
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)0.2%

Sample

1st row1915-09-20
2nd row1964-07-31
3rd row1908-03-01
4th row1987-05-28
5th row1987-05-28

Common Values

ValueCountFrequency (%)
2000-12-20310
 
0.2%
1980-03-09300
 
0.2%
1987-06-11278
 
0.1%
1993-03-12268
 
0.1%
1966-02-08265
 
0.1%
1941-08-23263
 
0.1%
2013-04-28251
 
0.1%
1984-12-06237
 
0.1%
1994-03-22234
 
0.1%
1979-03-23231
 
0.1%
Other values (2029)195268
98.7%

Length

2022-09-05T10:41:14.049596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-12-20310
 
0.2%
1980-03-09300
 
0.2%
1987-06-11278
 
0.1%
1993-03-12268
 
0.1%
1966-02-08265
 
0.1%
1941-08-23263
 
0.1%
2013-04-28251
 
0.1%
1984-12-06237
 
0.1%
1994-03-22234
 
0.1%
1979-03-23231
 
0.1%
Other values (2029)195268
98.7%

Most occurring characters

ValueCountFrequency (%)
-395810
20.0%
1383431
19.4%
0325949
16.5%
9239096
12.1%
2201102
10.2%
380444
 
4.1%
777012
 
3.9%
471211
 
3.6%
570900
 
3.6%
869204
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1583240
80.0%
Dash Punctuation395810
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1383431
24.2%
0325949
20.6%
9239096
15.1%
2201102
12.7%
380444
 
5.1%
777012
 
4.9%
471211
 
4.5%
570900
 
4.5%
869204
 
4.4%
664891
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
-395810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1979050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-395810
20.0%
1383431
19.4%
0325949
16.5%
9239096
12.1%
2201102
10.2%
380444
 
4.1%
777012
 
3.9%
471211
 
3.6%
570900
 
3.6%
869204
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1979050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-395810
20.0%
1383431
19.4%
0325949
16.5%
9239096
12.1%
2201102
10.2%
380444
 
4.1%
777012
 
3.9%
471211
 
3.6%
570900
 
3.6%
869204
 
3.5%

ssn_orig
Categorical

HIGH CARDINALITY

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
163-03-5043
 
310
150-86-0169
 
278
324-13-1462
 
207
700-35-1496
 
207
723-87-1156
 
207
Other values (2085)
196696 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2176955
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)0.2%

Sample

1st row054-77-5471
2nd row081-98-1984
3rd row791-54-9738
4th row850-93-3097
5th row850-93-3097

Common Values

ValueCountFrequency (%)
163-03-5043310
 
0.2%
150-86-0169278
 
0.1%
324-13-1462207
 
0.1%
700-35-1496207
 
0.1%
723-87-1156207
 
0.1%
712-09-6563207
 
0.1%
550-55-1944206
 
0.1%
497-97-6488206
 
0.1%
063-02-5690206
 
0.1%
893-78-8351206
 
0.1%
Other values (2080)195665
98.9%

Length

2022-09-05T10:41:14.291027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
163-03-5043310
 
0.2%
150-86-0169278
 
0.1%
324-13-1462207
 
0.1%
712-09-6563207
 
0.1%
700-35-1496207
 
0.1%
723-87-1156207
 
0.1%
691-96-0031206
 
0.1%
550-55-1944206
 
0.1%
584-23-1806206
 
0.1%
022-09-1662206
 
0.1%
Other values (2080)195665
98.9%

Most occurring characters

ValueCountFrequency (%)
-395810
18.2%
5187675
8.6%
2183567
8.4%
0183258
8.4%
4181244
8.3%
7179684
8.3%
8177008
8.1%
3175816
8.1%
6173955
8.0%
1171246
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1781145
81.8%
Dash Punctuation395810
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5187675
10.5%
2183567
10.3%
0183258
10.3%
4181244
10.2%
7179684
10.1%
8177008
9.9%
3175816
9.9%
6173955
9.8%
1171246
9.6%
9167692
9.4%
Dash Punctuation
ValueCountFrequency (%)
-395810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2176955
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-395810
18.2%
5187675
8.6%
2183567
8.4%
0183258
8.4%
4181244
8.3%
7179684
8.3%
8177008
8.1%
3175816
8.1%
6173955
8.0%
1171246
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2176955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-395810
18.2%
5187675
8.6%
2183567
8.4%
0183258
8.4%
4181244
8.3%
7179684
8.3%
8177008
8.1%
3175816
8.1%
6173955
8.0%
1171246
7.9%

lon_orig
Real number (ℝ)

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.996455728
Minimum-179.916516
Maximum179.828172
Zeros0
Zeros (%)0.0%
Negative99532
Negative (%)50.3%
Memory size3.0 MiB
2022-09-05T10:41:14.572755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-179.916516
5-th percentile-166.253318
Q1-89.615312
median-1.881417
Q385.797743
95-th percentile159.886936
Maximum179.828172
Range359.744688
Interquartile range (IQR)175.413055

Descriptive statistics

Standard deviation104.1711113
Coefficient of variation (CV)-34.76477569
Kurtosis-1.183976737
Mean-2.996455728
Median Absolute Deviation (MAD)87.733895
Skewness0.003815692959
Sum-593013.5709
Variance10851.62042
MonotonicityNot monotonic
2022-09-05T10:41:14.877330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-111.880543310
 
0.2%
169.915619278
 
0.1%
-59.699465207
 
0.1%
-82.055478207
 
0.1%
146.085866207
 
0.1%
126.682414207
 
0.1%
-151.330069206
 
0.1%
-58.547696206
 
0.1%
132.920961206
 
0.1%
1.971809206
 
0.1%
Other values (2080)195665
98.9%
ValueCountFrequency (%)
-179.916516103
0.1%
-179.8151178
0.1%
-179.748656103
0.1%
-179.655478103
0.1%
-179.626986104
0.1%
-179.5619211
 
< 0.1%
-179.415897103
0.1%
-179.300152103
0.1%
-178.894869104
0.1%
-178.836291157
0.1%
ValueCountFrequency (%)
179.828172103
0.1%
179.6220351
 
< 0.1%
179.306605103
0.1%
179.087506103
0.1%
179.067958103
0.1%
178.9661251
 
< 0.1%
178.800968103
0.1%
178.665559138
0.1%
178.516101104
0.1%
178.3328071
 
< 0.1%

lat_orig
Real number (ℝ)

Distinct2090
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.716914727
Minimum-89.9007205
Maximum89.990594
Zeros0
Zeros (%)0.0%
Negative101507
Negative (%)51.3%
Memory size3.0 MiB
2022-09-05T10:41:15.221221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-89.9007205
5-th percentile-81.3917675
Q1-48.03048
median-2.073738
Q344.8270885
95-th percentile80.655307
Maximum89.990594
Range179.8913145
Interquartile range (IQR)92.8575685

Descriptive statistics

Standard deviation52.30936772
Coefficient of variation (CV)-30.46707382
Kurtosis-1.226833804
Mean-1.716914727
Median Absolute Deviation (MAD)46.1556405
Skewness0.03914792872
Sum-339786.009
Variance2736.269952
MonotonicityNot monotonic
2022-09-05T10:41:15.562018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-85.03082310
 
0.2%
-57.380361278
 
0.1%
71.815678207
 
0.1%
-33.9602725207
 
0.1%
51.8139515207
 
0.1%
43.6196455207
 
0.1%
-75.6182255206
 
0.1%
-18.68243206
 
0.1%
46.9042895206
 
0.1%
-86.9604935206
 
0.1%
Other values (2080)195665
98.9%
ValueCountFrequency (%)
-89.9007205110
0.1%
-89.8762145113
0.1%
-89.766022103
0.1%
-89.7646755103
0.1%
-89.611902103
0.1%
-89.474081103
0.1%
-89.4265481
 
< 0.1%
-89.3783585103
0.1%
-89.187009127
0.1%
-89.174158103
0.1%
ValueCountFrequency (%)
89.9905941
 
< 0.1%
89.7979395103
0.1%
89.77011851
 
< 0.1%
89.7183295175
0.1%
89.689003116
0.1%
89.5224205103
0.1%
89.362151
 
< 0.1%
89.193606103
0.1%
89.1800835161
0.1%
89.08698251
 
< 0.1%

acct_id_bene
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.6764811
Minimum0
Maximum11991
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:15.892823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q124
median53
Q3191
95-th percentile4149
Maximum11991
Range11991
Interquartile range (IQR)167

Descriptive statistics

Standard deviation1695.983714
Coefficient of variation (CV)2.977099757
Kurtosis19.95378574
Mean569.6764811
Median Absolute Deviation (MAD)38
Skewness4.364975752
Sum112741824
Variance2876360.758
MonotonicityNot monotonic
2022-09-05T10:41:16.254430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253563
 
1.8%
203263
 
1.6%
133210
 
1.6%
143103
 
1.6%
273017
 
1.5%
172953
 
1.5%
242917
 
1.5%
232917
 
1.5%
182800
 
1.4%
122682
 
1.4%
Other values (4067)167480
84.6%
ValueCountFrequency (%)
021
 
< 0.1%
1293
 
0.1%
2420
 
0.2%
31342
0.7%
4859
0.4%
51295
0.7%
61378
0.7%
71746
0.9%
81659
0.8%
91563
0.8%
ValueCountFrequency (%)
119911
 
< 0.1%
119901
 
< 0.1%
119741
 
< 0.1%
118763
 
< 0.1%
118711
 
< 0.1%
118581
 
< 0.1%
118401
 
< 0.1%
1182270
< 0.1%
117461
 
< 0.1%
117021
 
< 0.1%

dsply_nm_bene
Categorical

HIGH CARDINALITY

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
C_25
 
3563
C_20
 
3263
C_13
 
3210
C_14
 
3103
C_27
 
3017
Other values (4072)
181749 

Length

Max length7
Median length4
Mean length4.419332508
Min length3

Characters and Unicode

Total characters874608
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1462 ?
Unique (%)0.7%

Sample

1st rowC_170
2nd rowC_23
3rd rowC_12
4th rowC_6503
5th rowC_6503

Common Values

ValueCountFrequency (%)
C_253563
 
1.8%
C_203263
 
1.6%
C_133210
 
1.6%
C_143103
 
1.6%
C_273017
 
1.5%
C_172953
 
1.5%
C_232917
 
1.5%
C_242917
 
1.5%
C_182800
 
1.4%
C_122682
 
1.4%
Other values (4067)167480
84.6%

Length

2022-09-05T10:41:16.591730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c_253563
 
1.8%
c_203263
 
1.6%
c_133210
 
1.6%
c_143103
 
1.6%
c_273017
 
1.5%
c_172953
 
1.5%
c_232917
 
1.5%
c_242917
 
1.5%
c_182800
 
1.4%
c_122682
 
1.4%
Other values (4067)167480
84.6%

Most occurring characters

ValueCountFrequency (%)
C197905
22.6%
_197905
22.6%
188710
10.1%
267842
 
7.8%
353856
 
6.2%
451552
 
5.9%
542801
 
4.9%
638604
 
4.4%
736673
 
4.2%
933151
 
3.8%
Other values (2)65609
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478798
54.7%
Uppercase Letter197905
22.6%
Connector Punctuation197905
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
188710
18.5%
267842
14.2%
353856
11.2%
451552
10.8%
542801
8.9%
638604
8.1%
736673
7.7%
933151
 
6.9%
833148
 
6.9%
032461
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
C197905
100.0%
Connector Punctuation
ValueCountFrequency (%)
_197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common676703
77.4%
Latin197905
 
22.6%

Most frequent character per script

Common
ValueCountFrequency (%)
_197905
29.2%
188710
13.1%
267842
 
10.0%
353856
 
8.0%
451552
 
7.6%
542801
 
6.3%
638604
 
5.7%
736673
 
5.4%
933151
 
4.9%
833148
 
4.9%
Latin
ValueCountFrequency (%)
C197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII874608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C197905
22.6%
_197905
22.6%
188710
10.1%
267842
 
7.8%
353856
 
6.2%
451552
 
5.9%
542801
 
4.9%
638604
 
4.4%
736673
 
4.2%
933151
 
3.8%
Other values (2)65609
 
7.5%

type_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
I
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I197905
100.0%

Length

2022-09-05T10:41:16.896079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:17.143277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
i197905
100.0%

Most occurring characters

ValueCountFrequency (%)
I197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter197905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin197905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I197905
100.0%

acct_stat_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
A
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A197905
100.0%

Length

2022-09-05T10:41:17.371091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:17.645018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a197905
100.0%

Most occurring characters

ValueCountFrequency (%)
A197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter197905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin197905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A197905
100.0%

acct_rptng_crncy_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 MiB
USD
197905 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters593715
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD197905
100.0%

Length

2022-09-05T10:41:17.864000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:18.103746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
usd197905
100.0%

Most occurring characters

ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter593715
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin593715
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII593715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U197905
33.3%
S197905
33.3%
D197905
33.3%

prior_sar_count_bene
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
162053 
True
35852 
ValueCountFrequency (%)
False162053
81.9%
True35852
 
18.1%
2022-09-05T10:41:18.315038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

branch_id_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
1
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1197905
100.0%

Length

2022-09-05T10:41:18.525636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:18.781716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1197905
100.0%

Most occurring characters

ValueCountFrequency (%)
1197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number197905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common197905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1197905
100.0%

open_dt_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.5 MiB
0
197905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0197905
100.0%

Length

2022-09-05T10:41:19.044129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:19.297039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0197905
100.0%

Most occurring characters

ValueCountFrequency (%)
0197905
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number197905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0197905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common197905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0197905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII197905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0197905
100.0%

close_dt_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1000000
197905 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1385335
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000000
2nd row1000000
3rd row1000000
4th row1000000
5th row1000000

Common Values

ValueCountFrequency (%)
1000000197905
100.0%

Length

2022-09-05T10:41:19.494744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:19.747316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1000000197905
100.0%

Most occurring characters

ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1385335
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common1385335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1385335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01187430
85.7%
1197905
 
14.3%

initial_deposit_bene
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74310.10622
Minimum50001.55
Maximum99999.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:19.984604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50001.55
5-th percentile52405.77
Q162535.57
median71210.53
Q388048.5
95-th percentile97896.53
Maximum99999.31
Range49997.76
Interquartile range (IQR)25512.93

Descriptive statistics

Standard deviation14760.15747
Coefficient of variation (CV)0.198629207
Kurtosis-1.211244726
Mean74310.10622
Median Absolute Deviation (MAD)13116.03
Skewness0.1628954081
Sum1.470634157 × 1010
Variance217862248.5
MonotonicityNot monotonic
2022-09-05T10:41:20.310818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67858.543563
 
1.8%
51562.383263
 
1.6%
64631.33210
 
1.6%
97896.533103
 
1.6%
56983.453017
 
1.5%
62535.572953
 
1.5%
89199.142917
 
1.5%
91434.672917
 
1.5%
62618.62800
 
1.4%
52909.532682
 
1.4%
Other values (4067)167480
84.6%
ValueCountFrequency (%)
50001.552
 
< 0.1%
50003.895
 
< 0.1%
50020.255
 
< 0.1%
50028.145
 
< 0.1%
50034.041
 
< 0.1%
500502
 
< 0.1%
50057.78
< 0.1%
50058.61
 
< 0.1%
50066.612
 
< 0.1%
50070.5417
< 0.1%
ValueCountFrequency (%)
99999.317
 
< 0.1%
99994.472
 
< 0.1%
99984.6810
 
< 0.1%
99961.382
 
< 0.1%
99951.814
 
< 0.1%
99932.7614
 
< 0.1%
99928.311
 
< 0.1%
99918.74107
0.1%
99915.41
 
< 0.1%
99868.47168
0.1%

tx_behavior_id_bene
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing197905
Missing (%)100.0%
Memory size3.0 MiB

bank_id_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
bank
197905 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters791620
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbank
2nd rowbank
3rd rowbank
4th rowbank
5th rowbank

Common Values

ValueCountFrequency (%)
bank197905
100.0%

Length

2022-09-05T10:41:20.615651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:20.846467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bank197905
100.0%

Most occurring characters

ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter791620
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin791620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII791620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b197905
25.0%
a197905
25.0%
n197905
25.0%
k197905
25.0%

first_name_bene
Categorical

HIGH CARDINALITY

Distinct582
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Michael
 
11473
John
 
6963
Joseph
 
5359
Ashley
 
4426
Thomas
 
4203
Other values (577)
165481 

Length

Max length11
Median length10
Mean length6.067451555
Min length3

Characters and Unicode

Total characters1200779
Distinct characters50
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st rowRachel
2nd rowAngel
3rd rowBrett
4th rowMegan
5th rowMegan

Common Values

ValueCountFrequency (%)
Michael11473
 
5.8%
John6963
 
3.5%
Joseph5359
 
2.7%
Ashley4426
 
2.2%
Thomas4203
 
2.1%
Steven4026
 
2.0%
Dawn3752
 
1.9%
Julie3595
 
1.8%
David3176
 
1.6%
Patrick3125
 
1.6%
Other values (572)147807
74.7%

Length

2022-09-05T10:41:21.065195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael11473
 
5.8%
john6963
 
3.5%
joseph5359
 
2.7%
ashley4426
 
2.2%
thomas4203
 
2.1%
steven4026
 
2.0%
dawn3752
 
1.9%
julie3595
 
1.8%
david3176
 
1.6%
patrick3125
 
1.6%
Other values (572)147807
74.7%

Most occurring characters

ValueCountFrequency (%)
e137074
 
11.4%
a129621
 
10.8%
n99432
 
8.3%
i90468
 
7.5%
r71759
 
6.0%
l71393
 
5.9%
h69395
 
5.8%
t62319
 
5.2%
o52251
 
4.4%
s44217
 
3.7%
Other values (40)372850
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1002874
83.5%
Uppercase Letter197905
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e137074
13.7%
a129621
12.9%
n99432
9.9%
i90468
9.0%
r71759
7.2%
l71393
7.1%
h69395
 
6.9%
t62319
 
6.2%
o52251
 
5.2%
s44217
 
4.4%
Other values (16)174945
17.4%
Uppercase Letter
ValueCountFrequency (%)
J29022
14.7%
M22261
11.2%
A20764
10.5%
D17195
8.7%
C13598
 
6.9%
S13251
 
6.7%
B12256
 
6.2%
T11107
 
5.6%
R10740
 
5.4%
K9860
 
5.0%
Other values (14)37851
19.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1200779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e137074
 
11.4%
a129621
 
10.8%
n99432
 
8.3%
i90468
 
7.5%
r71759
 
6.0%
l71393
 
5.9%
h69395
 
5.8%
t62319
 
5.2%
o52251
 
4.4%
s44217
 
3.7%
Other values (40)372850
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1200779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e137074
 
11.4%
a129621
 
10.8%
n99432
 
8.3%
i90468
 
7.5%
r71759
 
6.0%
l71393
 
5.9%
h69395
 
5.8%
t62319
 
5.2%
o52251
 
4.4%
s44217
 
3.7%
Other values (40)372850
31.1%

last_name_bene
Categorical

HIGH CARDINALITY

Distinct874
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Fisher
 
6240
Jones
 
4497
Moore
 
4455
Smith
 
4293
Ellis
 
4267
Other values (869)
174153 

Length

Max length11
Median length10
Mean length5.867678937
Min length2

Characters and Unicode

Total characters1161243
Distinct characters51
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st rowDoyle
2nd rowFisher
3rd rowKerr
4th rowPerry
5th rowPerry

Common Values

ValueCountFrequency (%)
Fisher6240
 
3.2%
Jones4497
 
2.3%
Moore4455
 
2.3%
Smith4293
 
2.2%
Ellis4267
 
2.2%
Irwin3574
 
1.8%
Garcia3416
 
1.7%
West3353
 
1.7%
White3235
 
1.6%
Strong3148
 
1.6%
Other values (864)157427
79.5%

Length

2022-09-05T10:41:21.314022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fisher6240
 
3.2%
jones4497
 
2.3%
moore4455
 
2.3%
smith4293
 
2.2%
ellis4267
 
2.2%
irwin3574
 
1.8%
garcia3416
 
1.7%
west3353
 
1.7%
white3235
 
1.6%
strong3148
 
1.6%
Other values (864)157427
79.5%

Most occurring characters

ValueCountFrequency (%)
e116768
 
10.1%
r104917
 
9.0%
n99983
 
8.6%
a97635
 
8.4%
o81194
 
7.0%
i67050
 
5.8%
l65195
 
5.6%
s61730
 
5.3%
t46085
 
4.0%
h35095
 
3.0%
Other values (41)385591
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter963338
83.0%
Uppercase Letter197905
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e116768
12.1%
r104917
10.9%
n99983
10.4%
a97635
10.1%
o81194
8.4%
i67050
 
7.0%
l65195
 
6.8%
s61730
 
6.4%
t46085
 
4.8%
h35095
 
3.6%
Other values (16)187686
19.5%
Uppercase Letter
ValueCountFrequency (%)
M17617
 
8.9%
S16562
 
8.4%
G16210
 
8.2%
H14159
 
7.2%
C13858
 
7.0%
F13816
 
7.0%
W12603
 
6.4%
B12580
 
6.4%
P10533
 
5.3%
T8661
 
4.4%
Other values (15)61306
31.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1161243
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e116768
 
10.1%
r104917
 
9.0%
n99983
 
8.6%
a97635
 
8.4%
o81194
 
7.0%
i67050
 
5.8%
l65195
 
5.6%
s61730
 
5.3%
t46085
 
4.0%
h35095
 
3.0%
Other values (41)385591
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1161243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e116768
 
10.1%
r104917
 
9.0%
n99983
 
8.6%
a97635
 
8.4%
o81194
 
7.0%
i67050
 
5.8%
l65195
 
5.6%
s61730
 
5.3%
t46085
 
4.0%
h35095
 
3.0%
Other values (41)385591
33.2%

street_addr_bene
Categorical

HIGH CARDINALITY

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
37582 Ford Route Apt. 734
 
3563
3751 Rachel Canyon Suite 408
 
3263
45229 Drake Route Apt. 113
 
3210
263 Wilson View Apt. 873
 
3103
16448 Audrey Road
 
3017
Other values (4072)
181749 

Length

Max length36
Median length31
Mean length22.66637023
Min length12

Characters and Unicode

Total characters4485788
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1462 ?
Unique (%)0.7%

Sample

1st row27020 Ashley Springs Apt. 150
2nd row35833 Kelly Manor
3rd row29456 Kelly Neck Apt. 042
4th row0952 Kevin Village Apt. 324
5th row0952 Kevin Village Apt. 324

Common Values

ValueCountFrequency (%)
37582 Ford Route Apt. 7343563
 
1.8%
3751 Rachel Canyon Suite 4083263
 
1.6%
45229 Drake Route Apt. 1133210
 
1.6%
263 Wilson View Apt. 8733103
 
1.6%
16448 Audrey Road3017
 
1.5%
8967 Lawson Fort2953
 
1.5%
35833 Kelly Manor2917
 
1.5%
37580 Ortiz Mall Suite 7352917
 
1.5%
45007 Thomas Way2800
 
1.4%
29456 Kelly Neck Apt. 0422682
 
1.4%
Other values (4067)167480
84.6%

Length

2022-09-05T10:41:21.591845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apt56258
 
7.1%
suite44604
 
5.6%
jennifer7437
 
0.9%
route6889
 
0.9%
canyon6300
 
0.8%
squares6257
 
0.8%
kelly6015
 
0.8%
road4493
 
0.6%
ford4025
 
0.5%
wilson3798
 
0.5%
Other values (4803)649363
81.6%

Most occurring characters

ValueCountFrequency (%)
597534
 
13.3%
e284098
 
6.3%
a212197
 
4.7%
t196414
 
4.4%
r188157
 
4.2%
i171641
 
3.8%
n164156
 
3.7%
s148770
 
3.3%
o147659
 
3.3%
5136075
 
3.0%
Other values (53)2239087
49.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2207163
49.2%
Decimal Number1128161
25.1%
Space Separator597534
 
13.3%
Uppercase Letter496672
 
11.1%
Other Punctuation56258
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e284098
12.9%
a212197
9.6%
t196414
8.9%
r188157
 
8.5%
i171641
 
7.8%
n164156
 
7.4%
s148770
 
6.7%
o147659
 
6.7%
l131682
 
6.0%
u90218
 
4.1%
Other values (16)472171
21.4%
Uppercase Letter
ValueCountFrequency (%)
S82071
16.5%
A77433
15.6%
R34465
 
6.9%
M32364
 
6.5%
C30597
 
6.2%
T25029
 
5.0%
P24953
 
5.0%
F20439
 
4.1%
J20290
 
4.1%
B18131
 
3.7%
Other values (15)130900
26.4%
Decimal Number
ValueCountFrequency (%)
5136075
12.1%
6119993
10.6%
3117265
10.4%
4114926
10.2%
2114116
10.1%
0112404
10.0%
8107573
9.5%
7107110
9.5%
1100300
8.9%
998399
8.7%
Space Separator
ValueCountFrequency (%)
597534
100.0%
Other Punctuation
ValueCountFrequency (%)
.56258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2703835
60.3%
Common1781953
39.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e284098
 
10.5%
a212197
 
7.8%
t196414
 
7.3%
r188157
 
7.0%
i171641
 
6.3%
n164156
 
6.1%
s148770
 
5.5%
o147659
 
5.5%
l131682
 
4.9%
u90218
 
3.3%
Other values (41)968843
35.8%
Common
ValueCountFrequency (%)
597534
33.5%
5136075
 
7.6%
6119993
 
6.7%
3117265
 
6.6%
4114926
 
6.4%
2114116
 
6.4%
0112404
 
6.3%
8107573
 
6.0%
7107110
 
6.0%
1100300
 
5.6%
Other values (2)154657
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4485788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597534
 
13.3%
e284098
 
6.3%
a212197
 
4.7%
t196414
 
4.4%
r188157
 
4.2%
i171641
 
3.8%
n164156
 
3.7%
s148770
 
3.3%
o147659
 
3.3%
5136075
 
3.0%
Other values (53)2239087
49.9%

city_bene
Categorical

HIGH CARDINALITY

Distinct3546
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
Port Hollymouth
 
3563
East Clayton
 
3263
North Paul
 
3212
South Raymondborough
 
3103
New Daniel
 
3024
Other values (3541)
181740 

Length

Max length24
Median length20
Mean length12.05695662
Min length6

Characters and Unicode

Total characters2386132
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1131 ?
Unique (%)0.6%

Sample

1st rowPort Jeremy
2nd rowPort Davidtown
3rd rowWest Richardmouth
4th rowSouth Oscarhaven
5th rowSouth Oscarhaven

Common Values

ValueCountFrequency (%)
Port Hollymouth3563
 
1.8%
East Clayton3263
 
1.6%
North Paul3212
 
1.6%
South Raymondborough3103
 
1.6%
New Daniel3024
 
1.5%
Lake Nicoleburgh2953
 
1.5%
Stephanieland2917
 
1.5%
Port Davidtown2917
 
1.5%
Lake Hollystad2800
 
1.4%
Michaelfort2742
 
1.4%
Other values (3536)167411
84.6%

Length

2022-09-05T10:41:21.887374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
port21164
 
7.1%
east17769
 
5.9%
north17106
 
5.7%
lake15065
 
5.0%
west11917
 
4.0%
new10610
 
3.5%
south8530
 
2.8%
michael3936
 
1.3%
hollymouth3563
 
1.2%
daniel3523
 
1.2%
Other values (2889)186883
62.3%

Most occurring characters

ValueCountFrequency (%)
e217997
 
9.1%
t187598
 
7.9%
a184780
 
7.7%
o176097
 
7.4%
r174798
 
7.3%
i128539
 
5.4%
h125715
 
5.3%
n113539
 
4.8%
l111140
 
4.7%
s104440
 
4.4%
Other values (40)861489
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1983905
83.1%
Uppercase Letter300066
 
12.6%
Space Separator102161
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e217997
11.0%
t187598
9.5%
a184780
9.3%
o176097
 
8.9%
r174798
 
8.8%
i128539
 
6.5%
h125715
 
6.3%
n113539
 
5.7%
l111140
 
5.6%
s104440
 
5.3%
Other values (16)459262
23.1%
Uppercase Letter
ValueCountFrequency (%)
N35629
11.9%
P29293
9.8%
S22603
 
7.5%
L22487
 
7.5%
W22203
 
7.4%
E21988
 
7.3%
M21543
 
7.2%
D20844
 
6.9%
J19480
 
6.5%
R14075
 
4.7%
Other values (13)69921
23.3%
Space Separator
ValueCountFrequency (%)
102161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2283971
95.7%
Common102161
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e217997
 
9.5%
t187598
 
8.2%
a184780
 
8.1%
o176097
 
7.7%
r174798
 
7.7%
i128539
 
5.6%
h125715
 
5.5%
n113539
 
5.0%
l111140
 
4.9%
s104440
 
4.6%
Other values (39)759328
33.2%
Common
ValueCountFrequency (%)
102161
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2386132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e217997
 
9.1%
t187598
 
7.9%
a184780
 
7.7%
o176097
 
7.4%
r174798
 
7.3%
i128539
 
5.4%
h125715
 
5.3%
n113539
 
4.8%
l111140
 
4.7%
s104440
 
4.4%
Other values (40)861489
36.1%

state_bene
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
MS
 
9997
NY
 
8240
AZ
 
8041
MT
 
7755
CA
 
7516
Other values (46)
156356 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters395810
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowME
2nd rowCA
3rd rowMT
4th rowMO
5th rowMO

Common Values

ValueCountFrequency (%)
MS9997
 
5.1%
NY8240
 
4.2%
AZ8041
 
4.1%
MT7755
 
3.9%
CA7516
 
3.8%
WY6827
 
3.4%
SC6363
 
3.2%
ME6145
 
3.1%
ND6043
 
3.1%
IL5374
 
2.7%
Other values (41)125604
63.5%

Length

2022-09-05T10:41:22.165532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ms9997
 
5.1%
ny8240
 
4.2%
az8041
 
4.1%
mt7755
 
3.9%
ca7516
 
3.8%
wy6827
 
3.4%
sc6363
 
3.2%
me6145
 
3.1%
nd6043
 
3.1%
il5374
 
2.7%
Other values (41)125604
63.5%

Most occurring characters

ValueCountFrequency (%)
A42182
 
10.7%
M41844
 
10.6%
N41229
 
10.4%
I27679
 
7.0%
T25148
 
6.4%
D24529
 
6.2%
S23676
 
6.0%
C22625
 
5.7%
L18770
 
4.7%
Y16754
 
4.2%
Other values (14)111374
28.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter395810
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A42182
 
10.7%
M41844
 
10.6%
N41229
 
10.4%
I27679
 
7.0%
T25148
 
6.4%
D24529
 
6.2%
S23676
 
6.0%
C22625
 
5.7%
L18770
 
4.7%
Y16754
 
4.2%
Other values (14)111374
28.1%

Most occurring scripts

ValueCountFrequency (%)
Latin395810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A42182
 
10.7%
M41844
 
10.6%
N41229
 
10.4%
I27679
 
7.0%
T25148
 
6.4%
D24529
 
6.2%
S23676
 
6.0%
C22625
 
5.7%
L18770
 
4.7%
Y16754
 
4.2%
Other values (14)111374
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII395810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A42182
 
10.7%
M41844
 
10.6%
N41229
 
10.4%
I27679
 
7.0%
T25148
 
6.4%
D24529
 
6.2%
S23676
 
6.0%
C22625
 
5.7%
L18770
 
4.7%
Y16754
 
4.2%
Other values (14)111374
28.1%

country_bene
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
US
197905 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters395810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US197905
100.0%

Length

2022-09-05T10:41:22.428314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:22.666635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
us197905
100.0%

Most occurring characters

ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter395810
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin395810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII395810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U197905
50.0%
S197905
50.0%

zip_bene
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4010
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50422.24855
Minimum520
Maximum99947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-09-05T10:41:22.922036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile5710
Q126581
median50399
Q375181
95-th percentile93812
Maximum99947
Range99427
Interquartile range (IQR)48600

Descriptive statistics

Standard deviation28696.92827
Coefficient of variation (CV)0.5691322601
Kurtosis-1.227654096
Mean50422.24855
Median Absolute Deviation (MAD)24584
Skewness0.04988004321
Sum9978815099
Variance823513692.3
MonotonicityNot monotonic
2022-09-05T10:41:23.259535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396333563
 
1.8%
663533263
 
1.6%
734393210
 
1.6%
287983103
 
1.6%
533773017
 
1.5%
752462953
 
1.5%
925352917
 
1.5%
147372917
 
1.5%
806872800
 
1.4%
147422682
 
1.4%
Other values (4000)167480
84.6%
ValueCountFrequency (%)
5201
 
< 0.1%
5293
 
< 0.1%
5893
 
< 0.1%
608225
0.1%
6121
 
< 0.1%
6768
 
< 0.1%
7511
 
< 0.1%
7862
 
< 0.1%
7901
 
< 0.1%
8321
 
< 0.1%
ValueCountFrequency (%)
999474
 
< 0.1%
9987314
< 0.1%
998681
 
< 0.1%
998331
 
< 0.1%
9981610
< 0.1%
998093
 
< 0.1%
997741
 
< 0.1%
997542
 
< 0.1%
997384
 
< 0.1%
996782
 
< 0.1%

gender_bene
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.2 MiB
Female
100345 
Male
97560 

Length

Max length6
Median length6
Mean length5.014072408
Min length4

Characters and Unicode

Total characters992310
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female100345
50.7%
Male97560
49.3%

Length

2022-09-05T10:41:23.578942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T10:41:23.887575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female100345
50.7%
male97560
49.3%

Most occurring characters

ValueCountFrequency (%)
e298250
30.1%
a197905
19.9%
l197905
19.9%
F100345
 
10.1%
m100345
 
10.1%
M97560
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter794405
80.1%
Uppercase Letter197905
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e298250
37.5%
a197905
24.9%
l197905
24.9%
m100345
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F100345
50.7%
M97560
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin992310
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e298250
30.1%
a197905
19.9%
l197905
19.9%
F100345
 
10.1%
m100345
 
10.1%
M97560
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII992310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e298250
30.1%
a197905
19.9%
l197905
19.9%
F100345
 
10.1%
m100345
 
10.1%
M97560
 
9.8%

birth_date_bene
Categorical

HIGH CARDINALITY

Distinct3883
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size14.2 MiB
1947-03-18
 
3563
1947-10-11
 
3263
1972-09-21
 
3210
1968-08-11
 
3103
1944-02-22
 
3017
Other values (3878)
181749 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1979050
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1337 ?
Unique (%)0.7%

Sample

1st row1979-03-23
2nd row1981-03-08
3rd row1970-09-25
4th row1994-11-08
5th row1994-11-08

Common Values

ValueCountFrequency (%)
1947-03-183563
 
1.8%
1947-10-113263
 
1.6%
1972-09-213210
 
1.6%
1968-08-113103
 
1.6%
1944-02-223017
 
1.5%
1941-11-182953
 
1.5%
1981-03-082917
 
1.5%
1999-11-202917
 
1.5%
1920-01-132800
 
1.4%
1970-09-252682
 
1.4%
Other values (3873)167480
84.6%

Length

2022-09-05T10:41:24.115659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1947-03-183563
 
1.8%
1947-10-113263
 
1.6%
1972-09-213210
 
1.6%
1968-08-113103
 
1.6%
1944-02-223017
 
1.5%
1941-11-182953
 
1.5%
1981-03-082917
 
1.5%
1999-11-202917
 
1.5%
1920-01-132800
 
1.4%
1970-09-252682
 
1.4%
Other values (3873)167480
84.6%

Most occurring characters

ValueCountFrequency (%)
1410683
20.8%
-395810
20.0%
0303153
15.3%
9248239
12.5%
2188642
9.5%
485170
 
4.3%
378284
 
4.0%
877119
 
3.9%
771926
 
3.6%
564348
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1583240
80.0%
Dash Punctuation395810
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1410683
25.9%
0303153
19.1%
9248239
15.7%
2188642
11.9%
485170
 
5.4%
378284
 
4.9%
877119
 
4.9%
771926
 
4.5%
564348
 
4.1%
655676
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
-395810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1979050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1410683
20.8%
-395810
20.0%
0303153
15.3%
9248239
12.5%
2188642
9.5%
485170
 
4.3%
378284
 
4.0%
877119
 
3.9%
771926
 
3.6%
564348
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1979050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1410683
20.8%
-395810
20.0%
0303153
15.3%
9248239
12.5%
2188642
9.5%
485170
 
4.3%
378284
 
4.0%
877119
 
3.9%
771926
 
3.6%
564348
 
3.3%

ssn_bene
Categorical

HIGH CARDINALITY

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
559-78-2560
 
3563
117-19-6980
 
3263
808-75-3560
 
3210
388-47-8953
 
3103
300-57-6092
 
3017
Other values (4072)
181749 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2176955
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1462 ?
Unique (%)0.7%

Sample

1st row322-84-8146
2nd row427-95-2332
3rd row346-44-2037
4th row010-77-8442
5th row010-77-8442

Common Values

ValueCountFrequency (%)
559-78-25603563
 
1.8%
117-19-69803263
 
1.6%
808-75-35603210
 
1.6%
388-47-89533103
 
1.6%
300-57-60923017
 
1.5%
845-39-66862953
 
1.5%
158-90-73102917
 
1.5%
427-95-23322917
 
1.5%
483-20-03322800
 
1.4%
346-44-20372682
 
1.4%
Other values (4067)167480
84.6%

Length

2022-09-05T10:41:24.387515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
559-78-25603563
 
1.8%
117-19-69803263
 
1.6%
808-75-35603210
 
1.6%
388-47-89533103
 
1.6%
300-57-60923017
 
1.5%
845-39-66862953
 
1.5%
158-90-73102917
 
1.5%
427-95-23322917
 
1.5%
483-20-03322800
 
1.4%
346-44-20372682
 
1.4%
Other values (4067)167480
84.6%

Most occurring characters

ValueCountFrequency (%)
-395810
18.2%
8201874
9.3%
0191083
8.8%
3188989
8.7%
7187959
8.6%
5183069
8.4%
6181016
8.3%
1173699
8.0%
2168368
7.7%
4161416
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1781145
81.8%
Dash Punctuation395810
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8201874
11.3%
0191083
10.7%
3188989
10.6%
7187959
10.6%
5183069
10.3%
6181016
10.2%
1173699
9.8%
2168368
9.5%
4161416
9.1%
9143672
8.1%
Dash Punctuation
ValueCountFrequency (%)
-395810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2176955
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-395810
18.2%
8201874
9.3%
0191083
8.8%
3188989
8.7%
7187959
8.6%
5183069
8.4%
6181016
8.3%
1173699
8.0%
2168368
7.7%
4161416
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2176955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-395810
18.2%
8201874
9.3%
0191083
8.8%
3188989
8.7%
7187959
8.6%
5183069
8.4%
6181016
8.3%
1173699
8.0%
2168368
7.7%
4161416
7.4%

lon_bene
Real number (ℝ)

HIGH CORRELATION

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.39381994
Minimum-179.916516
Maximum179.964418
Zeros0
Zeros (%)0.0%
Negative90886
Negative (%)45.9%
Memory size3.0 MiB
2022-09-05T10:41:24.677951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-179.916516
5-th percentile-162.655577
Q1-85.065895
median9.70636
Q386.60361
95-th percentile155.184031
Maximum179.964418
Range359.880934
Interquartile range (IQR)171.669505

Descriptive statistics

Standard deviation101.4968216
Coefficient of variation (CV)42.39952215
Kurtosis-1.161064881
Mean2.39381994
Median Absolute Deviation (MAD)89.587768
Skewness-0.1245557045
Sum473748.9352
Variance10301.60478
MonotonicityNot monotonic
2022-09-05T10:41:24.981064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.7455113563
 
1.8%
123.87593263
 
1.6%
47.1168383210
 
1.6%
-98.7529783103
 
1.6%
125.0677383017
 
1.5%
26.2441822953
 
1.5%
34.0917232917
 
1.5%
137.3317912917
 
1.5%
-162.5702372800
 
1.4%
77.2076192682
 
1.4%
Other values (4067)167480
84.6%
ValueCountFrequency (%)
-179.9165161
 
< 0.1%
-179.88354892
 
< 0.1%
-179.815121
 
< 0.1%
-179.74865626
 
< 0.1%
-179.6269869
 
< 0.1%
-179.5619217
 
< 0.1%
-179.523927106
 
0.1%
-179.41589734
 
< 0.1%
-179.300152358
0.2%
-179.2256811
 
< 0.1%
ValueCountFrequency (%)
179.9644182
 
< 0.1%
179.82817217
< 0.1%
179.75406614
< 0.1%
179.7209531
 
< 0.1%
179.6429921
 
< 0.1%
179.3584161
 
< 0.1%
179.2250162
 
< 0.1%
179.2051561
 
< 0.1%
179.08750614
< 0.1%
179.0679582
 
< 0.1%

lat_bene
Real number (ℝ)

HIGH CORRELATION

Distinct4077
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5862502169
Minimum-89.9928075
Maximum89.990594
Zeros0
Zeros (%)0.0%
Negative95408
Negative (%)48.2%
Memory size3.0 MiB
2022-09-05T10:41:25.283447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-89.9928075
5-th percentile-79.749951
Q1-51.3219645
median1.888823
Q345.7156515
95-th percentile78.795143
Maximum89.990594
Range179.9834015
Interquartile range (IQR)97.037616

Descriptive statistics

Standard deviation53.06859423
Coefficient of variation (CV)-90.52208886
Kurtosis-1.309846563
Mean-0.5862502169
Median Absolute Deviation (MAD)49.9046965
Skewness0.03324069213
Sum-116021.8492
Variance2816.275693
MonotonicityNot monotonic
2022-09-05T10:41:25.598057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.54984853563
 
1.8%
23.29493353263
 
1.6%
-67.54181853210
 
1.6%
-61.8823853103
 
1.6%
78.7951433017
 
1.5%
12.9931622953
 
1.5%
23.3304282917
 
1.5%
-40.23725052917
 
1.5%
70.6050022800
 
1.4%
-58.8593232682
 
1.4%
Other values (4067)167480
84.6%
ValueCountFrequency (%)
-89.99280752
 
< 0.1%
-89.9459931
 
< 0.1%
-89.9007205692
0.3%
-89.87621453
 
< 0.1%
-89.80911252
 
< 0.1%
-89.7660221
 
< 0.1%
-89.76467551
 
< 0.1%
-89.66838146
 
< 0.1%
-89.61190225
 
< 0.1%
-89.53602810
 
< 0.1%
ValueCountFrequency (%)
89.9905941
 
< 0.1%
89.85280158
 
< 0.1%
89.79793951
 
< 0.1%
89.7781641
 
< 0.1%
89.77011851
 
< 0.1%
89.761382517
 
< 0.1%
89.70433151
 
< 0.1%
89.6890033
 
< 0.1%
89.620819571
< 0.1%
89.61841051
 
< 0.1%

Interactions

2022-09-05T10:40:05.578678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:21.329586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:30.731878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:39.962229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:49.415778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:00.499000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:14.800995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:25.852279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:39.387819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:50.507313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:13.327724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:27.312150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:42.681399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:54.775646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:00.146639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:05.971630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:22.000815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:31.307716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:40.644402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:50.065342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:01.398359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:15.870594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:26.990364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:40.422461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:54.886909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:14.339644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:28.291533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:43.688761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:55.168948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:00.536628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:06.334802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:22.548600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:31.888901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:41.318372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:50.732339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:02.279622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:17.588149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:28.323044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:41.324350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:58.685682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:15.285563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:29.223500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:44.627795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:55.536613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:00.904320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:06.691350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:23.129310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:32.562168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:41.923514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:51.367859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:03.166653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:18.274413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:29.439604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:42.354552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:01.285447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:16.211198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:30.198991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:45.556272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:55.897930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:01.258535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:07.043986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:23.668094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:33.196241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:42.470715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:52.040490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:04.269846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:18.877118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:30.633795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:43.247673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:02.623504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:17.090272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:31.128853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:46.620891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:56.238822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:01.600636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:07.392987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:24.225734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:33.781985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:43.009611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:52.731293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:05.236870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:19.500746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:31.573562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:43.919644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:03.928851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:17.986602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:33.286855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:47.562751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:56.592176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:01.971068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:07.767311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:24.894105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:34.426298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:43.657824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:53.335795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:06.209232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:20.165770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:32.226547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:44.569125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:05.222315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:18.946651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:34.244856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:48.546157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:56.954661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:02.325089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:08.119078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:25.470457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:34.987681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:44.304531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:53.880868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:07.168807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:20.816101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:32.965809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:45.181326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:05.929489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:19.886476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:35.233894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:49.495088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:57.315513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:02.686251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:08.472711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:25.945324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:35.630210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:44.948530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:54.412262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:08.222294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:21.441379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:33.526476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:45.807121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:07.003819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:20.799864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:36.165835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:50.389822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:57.661259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:03.052054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:08.821613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:26.592934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:36.181788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:45.685519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:54.966636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:09.192386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:22.072067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:34.272852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:46.396727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:08.384170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:21.742888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:37.028535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:51.258091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:58.029701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:03.415087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:09.183418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:27.125901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:36.827946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:46.353591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:55.982454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:10.124878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:22.721877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:35.166935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:46.995811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:09.140764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:22.635110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:37.979720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:52.168959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:58.393625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:03.779250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:09.549775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:27.659029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:37.480201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:46.917835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:56.925325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:11.044304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:23.302892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:35.847609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:47.581469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:09.751308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:23.603773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:38.861992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:53.004718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:58.745944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:04.138990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:10.029145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:28.273677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:38.150606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:47.535305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:57.789142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:11.967269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:23.898724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:36.653078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:48.157477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:10.712199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:24.525460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:39.784130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:53.599224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:59.096211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:04.488950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:10.499212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:28.868489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:38.741762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:48.098698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:58.690707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:12.905174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:24.529385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:37.330482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:49.003017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:11.419634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:25.432057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:40.751882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:54.005430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:59.459605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:04.829092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:10.924132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:30.147897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:39.317046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:48.733395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:37:59.598373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:13.819955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:25.121766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:38.422858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:38:49.699993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:12.399959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:26.343493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:41.714684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:54.389781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:39:59.806477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-05T10:40:05.199571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-09-05T10:41:26.043603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-05T10:41:27.916598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-05T10:41:28.848369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-05T10:41:29.583679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-05T10:41:30.250176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-05T10:40:17.416815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-05T10:40:46.214742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

tran_idorig_acctbene_accttx_typebase_amttran_timestampis_saralert_idacct_id_origdsply_nm_origtype_origacct_stat_origacct_rptng_crncy_origprior_sar_count_origbranch_id_origopen_dt_origclose_dt_originitial_deposit_origtx_behavior_id_origbank_id_origfirst_name_origlast_name_origstreet_addr_origcity_origstate_origcountry_origzip_origgender_origbirth_date_origssn_origlon_origlat_origacct_id_benedsply_nm_benetype_beneacct_stat_beneacct_rptng_crncy_beneprior_sar_count_benebranch_id_beneopen_dt_beneclose_dt_beneinitial_deposit_benetx_behavior_id_benebank_id_benefirst_name_benelast_name_benestreet_addr_benecity_benestate_benecountry_benezip_benegender_benebirth_date_benessn_benelon_benelat_bene
014376170TRANSFER885.302017-01-01T00:00:00ZFalse-14376C_4376IAUSDFalse10100000063446.28NaNbankKristinGentry9548 Hooper ManorsSarahmouthIAUS98692Female1915-09-20054-77-547190.06649934.092692170C_170IAUSDFalse10100000084168.61NaNbankRachelDoyle27020 Ashley Springs Apt. 150Port JeremyMEUS74661Female1979-03-23322-84-8146151.10122345.779955
12430023TRANSFER630.412017-01-01T00:00:00ZFalse-14300C_4300IAUSDFalse10100000079684.15NaNbankAnthonyTaylor337 Robinson OvalWest KevintownNMUS86415Male1964-07-31081-98-1984-22.1811478.67932023C_23IAUSDFalse10100000089199.14NaNbankAngelFisher35833 Kelly ManorPort DavidtownCAUS92535Female1981-03-08427-95-2332137.33179123.330428
23443312TRANSFER393.142017-01-01T00:00:00ZFalse-14433C_4433IAUSDFalse10100000064630.28NaNbankDianaGray82286 Cheryl StationNathanportMSUS87062Female1908-03-01791-54-9738-91.241754-9.06134312C_12IAUSDFalse10100000052909.53NaNbankBrettKerr29456 Kelly Neck Apt. 042West RichardmouthMTUS14742Male1970-09-25346-44-203777.207619-58.859323
3425526503TRANSFER659.742017-01-01T00:00:00ZFalse-12552C_2552IAUSDFalse10100000079188.34NaNbankDebbieJones35710 Gary SummitDavidburyRIUS54433Female1987-05-28850-93-3097-16.454245-39.8591876503C_6503IAUSDFalse10100000057537.45NaNbankMeganPerry0952 Kevin Village Apt. 324South OscarhavenMOUS85424Female1994-11-08010-77-8442-128.496888-44.919702
4525526503TRANSFER442.442017-01-01T00:00:00ZFalse-12552C_2552IAUSDFalse10100000079188.34NaNbankDebbieJones35710 Gary SummitDavidburyRIUS54433Female1987-05-28850-93-3097-16.454245-39.8591876503C_6503IAUSDFalse10100000057537.45NaNbankMeganPerry0952 Kevin Village Apt. 324South OscarhavenMOUS85424Female1994-11-08010-77-8442-128.496888-44.919702
5628175TRANSFER140.062017-01-01T00:00:00ZFalse-1281C_281IAUSDTrue10100000087074.80NaNbankTerriLivingston5466 Ray Orchard Suite 327DennisshireDCUS33427Female1943-12-03109-41-1827-177.699428-37.15459775C_75IAUSDFalse10100000050335.69NaNbankChristopherMayer8576 Branch IslandJonesburyDEUS99020Male1969-05-10725-70-344117.502090-77.490069
67553400TRANSFER612.572017-01-01T00:00:00ZFalse-1553C_553IAUSDFalse10100000076295.34NaNbankMonicaNewman55690 Jennifer Row Suite 574New VictoriastadHIUS70812Female1913-09-16776-46-68608.158774-53.763205400C_400IAUSDFalse10100000085853.72NaNbankVanessaGillespie898 Christina Lodge Apt. 119PerkinsportALUS13342Female1998-06-19593-64-5768149.69884620.289089
78240990TRANSFER665.622017-01-01T00:00:00ZFalse-1240C_240IAUSDFalse10100000076874.34NaNbankTravisRichardson780 Sanchez ForksKristinehavenWVUS4843Male1934-07-09250-25-5176-170.53214077.683888990C_990IAUSDFalse10100000070044.46NaNbankPatrickColeman4439 Lauren Via Apt. 396South MichaeltownAKUS7652Male1962-04-01696-54-8587-156.82927883.080294
89668132TRANSFER970.502017-01-01T00:00:00ZFalse-1668C_668IAUSDFalse10100000060794.76NaNbankJamieSmith64474 Anthony Ports Apt. 280JacksonhavenMSUS88877Female2003-10-14848-66-0746-30.67251023.072227132C_132IAUSDFalse10100000060784.17NaNbankScottTrujillo955 Wilson Turnpike Apt. 652BoothburyTXUS96682Male2008-01-19743-81-6309142.057062-86.030230
9102085104TRANSFER945.462017-01-01T00:00:00ZFalse-12085C_2085IAUSDFalse10100000088190.98NaNbankKennethCarroll532 White Mountain Apt. 758West MatthewtownOKUS50926Male1960-05-01155-63-5647155.566388-43.754245104C_104IAUSDTrue10100000067881.53NaNbankLukeVazquez94312 Dickson Falls Apt. 905GregoryviewVAUS92174Male1944-03-25811-77-3614-177.814657-51.498302

Last rows

tran_idorig_acctbene_accttx_typebase_amttran_timestampis_saralert_idacct_id_origdsply_nm_origtype_origacct_stat_origacct_rptng_crncy_origprior_sar_count_origbranch_id_origopen_dt_origclose_dt_originitial_deposit_origtx_behavior_id_origbank_id_origfirst_name_origlast_name_origstreet_addr_origcity_origstate_origcountry_origzip_origgender_origbirth_date_origssn_origlon_origlat_origacct_id_benedsply_nm_benetype_beneacct_stat_beneacct_rptng_crncy_beneprior_sar_count_benebranch_id_beneopen_dt_beneclose_dt_beneinitial_deposit_benetx_behavior_id_benebank_id_benefirst_name_benelast_name_benestreet_addr_benecity_benestate_benecountry_benezip_benegender_benebirth_date_benessn_benelon_benelat_bene
19789519789644364TRANSFER475.662018-12-21T00:00:00ZFalse-1443C_443IAUSDFalse10100000053563.64NaNbankMariaMorris01749 Hamilton Course Suite 336RyanmouthNVUS58712Female2007-06-25270-42-6506158.6957154.96074064C_64IAUSDFalse10100000091528.28NaNbankJohnKnight79148 Pierce Lock Suite 423ErikbergCAUS9478Male1962-01-08570-12-435178.928510-28.354113
19789619789746516029TRANSFER632.002018-12-21T00:00:00ZFalse-14651C_4651IAUSDFalse10100000074115.62NaNbankPatrickCarter819 Todd Springs Apt. 444East JasmineMIUS16810Male2021-03-27551-69-0740-114.04069987.3070686029C_6029IAUSDFalse10100000095516.71NaNbankRickyMoran6055 James MountainWest MichaelfurtKSUS73789Male1985-02-22596-73-9766-122.89976829.258724
19789719789826514TRANSFER188.372018-12-21T00:00:00ZFalse-1265C_265IAUSDFalse10100000050781.53NaNbankOscarLong10723 Duncan CornerLindaviewNEUS16047Male1906-10-18751-37-9051-19.16647421.54642214C_14IAUSDFalse10100000097896.53NaNbankLorettaGarcia263 Wilson View Apt. 873South RaymondboroughAZUS28798Female1968-08-11388-47-8953-98.752978-61.882385
19789819789910513TRANSFER116.022018-12-21T00:00:00ZFalse-1105C_105IAUSDTrue10100000082521.54NaNbankTeresaWalsh2278 Wesley ForgesSouth LeslieNVUS30836Female1943-09-12692-91-3093-126.1072143.89891813C_13IAUSDTrue10100000064631.30NaNbankJosephWest45229 Drake Route Apt. 113North PaulMOUS73439Male1972-09-21808-75-356047.116838-67.541819
197899197900675270TRANSFER502.132018-12-21T00:00:00ZFalse-1675C_675IAUSDFalse10100000070050.90NaNbankShannonFarmer4304 Rachel StationLake ArthurALUS98648Female1906-12-04023-13-984163.258068-48.716837270C_270IAUSDTrue10100000064211.03NaNbankSharonLester081 Jennifer KnollsLake JamieCTUS6500Female1965-02-14894-40-447729.02350073.279432
197900197901294851TRANSFER675.302018-12-21T00:00:00ZFalse-12948C_2948IAUSDFalse10100000073456.24NaNbankCraigDavies0223 Morris AlleyHaasstadORUS67265Male1973-01-05457-22-4927-36.38710217.81564151C_51IAUSDFalse10100000071122.41NaNbankThomasTaylor78209 Darlene Bypass Suite 137Port StephenVTUS44235Male2011-03-24570-08-8904-135.75295748.961165
1979011979022767688TRANSFER864.552018-12-21T00:00:00ZFalse-1276C_276IAUSDFalse10100000058993.69NaNbankDeborahTaylor2431 Chan Locks Suite 711East StephenMNUS99654Female1907-04-04713-48-7280-26.58924287.5135377688C_7688IAUSDFalse10100000064648.90NaNbankRyanNolan9178 Rios HarborsStephenfurtVAUS68882Male1968-05-06804-47-266412.712477-20.559546
197902197903885198TRANSFER682.872018-12-21T00:00:00ZFalse-1885C_885IAUSDFalse10100000051234.09NaNbankKathySoto84966 Lee SummitWest VictorGAUS10250Female1943-10-30505-26-3083132.292923-12.501948198C_198IAUSDTrue10100000085152.71NaNbankMaryArmstrong0663 Cassandra Turnpike Apt. 665BrockfurtKYUS62557Female1908-02-26089-05-2474150.92603360.113135
1979031979044278639TRANSFER780.682018-12-21T00:00:00ZFalse-14278C_4278IAUSDFalse10100000066156.31NaNbankJasonRussell338 Stark Place Apt. 354DanielsfurtKSUS20916Male1997-08-07029-78-0320-122.58837671.009383639C_639IAUSDFalse10100000096278.77NaNbankAnthonyBennett64140 Nathan Bridge Suite 735HeathertownUTUS48158Male2011-09-10022-09-16621.97180951.773742
19790419790550142TRANSFER459.012018-12-21T00:00:00ZFalse-1501C_501IAUSDFalse10100000088951.73NaNbankKellyHernandez0158 William Forest Suite 737East NathanielstadNVUS32156Female1919-07-20891-87-632650.219044-6.27067942C_42IAUSDFalse10100000070573.82NaNbankStevenPierce07154 Stephen Parkways Suite 265LindafurtMEUS70013Male1910-12-23792-69-9348116.403981-19.550031